Simply put, any algorithm that has the ability to learn on its own, given a set of data, without having to program the rules of the domain explicitly, falls under the ambit of Machine Learning. This is different from Data Analytics or Expert systems where, rules, logic, propositions or activities has to be manually coded by an expert programmer. Systems which has ability to learn on its own and progress towards a pre-defined goal, without much of human intervention can be broadly termed as Intelligent Systems. The quality of intelligence can range from an amoeba, algae, ant, armadillo all the way to chimps, humans or beyond. As an example, systems which interact with humans in natural language cannot be built by coding the rules and conversational logic of human language.

Will smart automation, intelligent software bots and brainy robots take away our jobs anytime soon? Pose this question to any Indian working in a company where unions are strong, or to any Indian who has a government job, or to the majority of Indians who work in the unorganized sector--those who drive taxis, trucks pull handcarts, hawk goods on footpaths or are employed as maids--and you will, in all probability, be looked at askance or even dismissed as an uninformed prophet of doom. The reaction may not be surprising in emerging countries like India, given that a majority of such employees would never have heard about the Industrial Revolution, or terms like disguised unemployment, cloud computing, machine learning, deep learning, automation or artificial intelligence (AI)-driven software bots. They would perhaps have also never heard of drones taking photographs and doing surveillance; of robots delivering pizzas and packages; of assistive robots taking care of the elderly; of robots making hamburgers and others like the Roomba robots that mop floors; of software bots writing articles and movie scripts; of three-dimensional or 3D printing revolutionizing the manufacturing sector; of driverless cars and trucks--all of which would make it very hard for them to imagine the future impact of these technologies that have not yet directly touched their lives or their jobs. They would have surely seen humanoid robots in sci-fi films like actor Rajnikant's Enthiran in Tamil or Robot in English, or a movie like Terminator or Transformers.

Condé Nast is partnering with Watson to find the right influencers for campaigns. Condé Nast is now tapping into Watson, IBM's super computer, to help build and strategize social influencer campaigns for brands. Through a new partnership announced today with IBM and the influencer platform Influential, brands advertising with the media company's properties--publications such as Vogue, Vanity Fair and The New Yorker--will be able to use big data to better know which social media celebrities might make for a good match for any given campaign. Using software built by IBM and Influential, Condé Nast's clients will be able to know which influencer's demographics, personality traits and more best align with a marketer and the audience it's targeting. "Within the dating sense of the word, we are matching people based on different data points," said Influential CEO Ryan Detert.

Suppose you were to watch a stream of numbers… and given the previous number you saw you had to predict what the next number should be. For example, suppose you saw 1,2,1,2,… We might guess: 1. Or perhaps, what if you saw 0,0,1,2,3…? Should it be 4? It almost feels like a domino effect. In this post we walk through predicting a specific type of spike in domain queries associated with some botnets.

One of the main goals of the machine learning team here at the Spark Technology Center is to continue to evolve Apache Spark as the foundation for end-to-end, continuous, intelligent enterprise applications. While working on adding multi-class logistic regression to Spark ML (part of the ongoing push towards parity between ml and mllib), STC team member Seth Hendrickson realized that, due to the way that Spark automatically serializes data when inter-node communication is required (e.g. during a reduce or aggregation operation), the aggregation step of the logistic regression training algorithm resulted in 3x more data being communicated than necessary. What does it mean when we refer to Apache Spark as the "foundation for end-to-end, continuous, intelligent enterprise applications"?

Now that the dust has settled on Apache Spark 2.0, the community has a chance to catch its collective breath and reflect a little on what was achieved for the largest and most complex release in the project's history. One of the main goals of the machine learning team here at the Spark Technology Center is to continue to evolve Apache Spark as the foundation for end-to-end, continuous, intelligent enterprise applications. With that in mind, we'll briefly mention some of the major new features in the 2.0 release in Spark's machine-learning library, MLlib, as well as a few important changes beneath the surface. Finally, we'll cast our minds forward to what may lie ahead for version 2.1 and beyond. For MLlib, there were a few major highlights in Spark 2.0: While these have already been well covered elsewhere, the STC team has worked hard to help make these initiatives a reality -- congratulations!

Hyperloglog and MinHash: Implementation of a form of hyperloglog and adding capabilities of MinHash algorithm on to it which would enable to perform set intersections."While it does require extra processing power to deal with collecting all the minima, it's possible to get satisfactory performance out of the structure for a relatively low storage or memory footprint" Ted Dunning's variant of Q-digest that does some improvements Distributed Streams Algorithms for Sliding Windows by Phillip B. Gibbons and Srikanta Tirthapura

I have recently presented my explorations of computer vision APIs(part 1, part 2, and part 3) on the AI meetup in Alpharetta. This time I decided to do something useful with it. When you work with digital platforms (be that content management, e-commerce, or digital assets) you can't go far without organizing your images. Tagging makes your assets library navigable and searchable. Descriptions are a great companion to the visual preview and can also serve as the alternate text. WCAG 2.0 requires non-text content to come with a text alternative for the very basic Level A compliance.

Dr. Yaji Sripada is the Chief Development Scientist of Arria NLG and one of the company's founders. He has worked on natural language generation for the past 25 years, and has published 75 peer-reviewed academic papers. Yaji is particularly interested in integrating NLG to adjacent technologies such as data analytics and information visualization, so we were keen to get his stance on this very topic. All aimed at supporting companies in their quest to make sense of business data. Yet the moment of clarity, when true insights are revealed from data, still eludes even sophisticated organizations.

Machine learning has arrived at a point where it is both accessible as well as affordable, says a Juniper study. This is according to a recent Juniper report tiled AI & Machine Learning: Media Dynamics, Disruption & Future Opportunities 2016-2021, which reveals machine learning; a subset of AI; has arrived at a point where it is both accessible as well as affordable to a wide range of stakeholders. Juniper anticipates the technology will eventually permeate into nearly all industries in the next five years. In the case of the media industry, machine learning is being used to develop so-called'bots' and digital assistants, as well as maximise returns on digital advertising, says Juniper. Companies such as Facebook and Google are leading the drive, with the likes of Rocket Fuel and Datacratic developing innovative solutions for digital assistant cases.